High-throughput precision phenotyping (HPP) is rapidly becoming popular in plant breeding for its ability to facilitate measurement of data points from a broad spectrum of light reflectance wavelengths. When the obtained data points correlate well with a phenotypic trait of interest (e.g., grain yield), this allows using this technology to evaluate several plant phenotypes of agronomical importance. For example, HPP can predict grain yield weeks before harvesting date, thus reducing harvesting costs and making it possible to make decisions for the next sowing season. However, it is not clear how spatial variability affects the relationship between HPP data and the traits of interest. It is known that soil characteristics can create a spurious relationship between different variables including HPP data. Spatial or soil variability can be handled by the experimental design and by the statistical model used to analyze the data, thereby improving the use of high-throughput phenotypic data.
The overall aim of this project is to understand and develop statistical models to analyze spatial variability of HPP data and their relationship with traits of interest, i.e., grain yield, flowering date. Specific objectives: (1) to test different spatial models and their ability to model HPP data; (2) to analyze the relationship between the HPP data, the spatial model and the experimental design; and (3) to publish scientific papers and technical documents to disseminate research results. The student will be responsible for collecting and curating the data, analyzing them and writing the documents. The student will work closely with BSU colleagues and give lectures to CIMMYT staff and partners.
Experimental design, linear mixed models.